Abstract
Oxygen and nitrogen are essential gases with extensive applications in industrial and medical fields, making their separation from air a critical yet complex challenge. Zeolites, renowned for their highly porous structures, have garnered significant attention as promising materials for enhancing gas storage and separation processes. In this paper, advanced machine learning (ML) methods, including Generalized Regression Neural Network (GRNN), Cascade Forward Neural Network, and Multilayer Perceptron were utilized to forecast the O(2) uptake capacity in zeolites. A comprehensive database of 750 experimental O(2) uptake values was constructed, incorporating pressure, pore volume, temperature, and surface area as input features to develop robust predictive models. The findings demonstrated the superior performance of the GRNN model, achieving an exceptional root mean square error of 0.03 and a coefficient of determination (R(2)) of 0.9991, outperforming the other techniques. Additionally, further analyses confirmed the reliability of the presented models in accurately capturing physical trends of O(2) uptake under varying pressure conditions at different constant temperatures. Sensitivity analysis further revealed that pressure positively influences O(2) storage, while temperature exerts the most significant effect, with relevancy factors of 0.341 and - 0.848, respectively. These results underscore the effectiveness of ML techniques in precisely forecasting and enhancing gas storage processes in zeolites, offering meaningful insights to drive advancements in separation technologies.